Ivan Stupia

Outage Probability and Energy Efficiency of Cooperative MIMO with Antenna Selection

This paper compares the energy efficiency of some cooperative MIMO schemes in wireless networks. By energy efficiency we denote the spectral efficiency seen at the receiver normalized by the total energy consumption, which includes the circuitry, the efficiency of the power amplifier, and the transmission rate. We focus on transmit antenna selection (TAS) and switch and stay combining (SSC) at the receiver. The performance of TAS+SSC is compared to that of TAS and maximal ratio combining (MRC), and to that of transmit/receive beamforming using a singular value decomposition (SVD) technique. We derive closed-form outage probability expressions, and analyze the effect of selecting the antenna from the source to optimize the communication with the relay or with the destination. Our results show that selecting the antennas with respect to the destination is in general a better option when the energy consumption is accounted. Moreover, some power allocation strategies are described and the comparison of the schemes in terms of energy efficiency reveals a considerable improvement with the use of TAS+SSC for low to moderate spectral efficiency, while beamforming outperforms the other schemes for high spectral efficiency when the number of antennas at each node is small.

A Novel Link Performance Prediction Method for Coded MIMO-OFDM Systems

Coded multi-antenna and multi-carrier techniques combined together with link resources adaptation algorithms are the key technologies toward efficient high-data-rate communications over wireless fading channels. The practicability of this concept, however, requires that the transmitter can perform accurate and simple evaluation of the actual link performance. This paper contributes with a novel method specifically developed to predict the link performance of bit-interleaved coded MIMO-OFDM links, which offers improved accuracy at the price of lower complexity when compared with conventional techniques. Its effectiveness is confirmed through extensive simulation results obtained over typical wireless channel environments.

Power Control in Networks With Heterogeneous Users: A Quasi-Variational Inequality Approach

This paper deals with the power allocation problem in a multipoint-to-multipoint network, which is heterogenous in the sense that each transmit and receiver pair can arbitrarily choose whether to selfishly maximize its own rate or energy efficiency. This is achieved by modeling the transmit and receiver pairs as rational players that engage in a noncooperative game in which the utility function changes according to each players nature. The underlying game is reformulated as a quasi variational inequality (QVI) problem using convex fractional program theory. The equivalence between the QVI and the noncooperative game provides us with all the mathematical tools to study the uniqueness of its Nash equilibrium points and to derive novel algorithms that allow the network to converge to these points in an iterative manner, both with and without the need for a centralized processing. Numerical results are used to validate the proposed solutions in different operating conditions.This work deals with the power allocation problem in a multipoint-to-multipoint network, which is heterogenous in the sense that each transmit and receiver pair can arbitrarily choose whether to selfishly maximize its own rate or energy efficiency. This is achieved by modeling the transmit and rec eiver pairs as rational players that engage in a non-cooperative game in which the utility function changes according to each players nature. The underlying game is reformulated as a quasi variational inequality (QVI) problem using convex fractional program theory. The equivalence between the QVI and the non- cooperative game provides us with all the mathematical tools to study the uniqueness of its Nash equilibrium (NE) points and to derive novel algorithms that allow the network to converge to these points in an iterative manner both with and without the need for a centralized processing. Numerical results are used to validate the proposed solutions in different operating conditions.

Power Allocation for Goodput Optimization in BICM-OFDM Systems

This paper deals with the power allocation problem for coded multicarrier transmission. Specifically, we focus on a bit interleaved coded modulation (BICM) packet transmission implemented with orthogonal frequency division multiplexing (OFDM) and in the presence of automatic repeat request (ARQ) protocol. Capitalizing on the binary-input output-symmetric (BIOS) nature of the BICM channel it is provided a simple upper-bound of the rate of information bits received without any error, the so called goodput. Based on this theoretical characterization, we develop a power allocation strategy among the different subcarriers so that the system goodput performance metric is maximized. The effectiveness of the proposed method is numerically testified for BICM-OFDM transmission in the context of the typical WLAN scenario.

A greedy algorithm for goodput-based adaptive modulation and coding in BIC-OFDM systems

This paper develops cross-layer adaptive modulation and coding as an efficient method for bit interleaved coded OFDM packet transmission. As objective function, we adopt the goodput, or the number of data bits delivered without error per unit of time. After the formalization of the optimization problem through an accurate modeling on the link performance, an iterative solution is derived based on the framework of greedy algorithms. Simulation results show that the proposed method improves upon the performance of static transmission and uniform bit allocation by 2–4 dB.

A game theoretical approach for coded cooperation in cognitive radio networks

In this paper, the authors focus on a game theoretical approach for cooperation in cognitive radio (CR) networks. In particular, an integrated design framework which relies on the effective SNR methodology is proposed. The authors derive a distributed power allocation policy aimed at maximizing the reliability of a cooperative BIC OFDM link wherein pragmatic modulation and coding schemes are considered. More in detail, the cognitive devices adapt their power to enable efficient cooperation and coexistence between cognitive nodes and primary networks. First of all, the gain due to the cooperation protocol is analytically derived, resorting to a simple first order recursive equation that depends on the current channel conditions. Then, after an accurate formalization of the optimization problem, a distributed iterative solution based on a novel algorithm, named Successive Set Reduction, is proposed. In particular, the authors show that : i) the proposed power allocation policy takes into account the cooperative gain through a simple scalar value, named cooperative effective SNR; ii) it is effective in improving the packet error rate performance with respect to other conventional power allocation strategies, thus allowing a better coverage for the secondary network; iii) the convergence of the distributed algorithm has exponential speed and requires only local signaling between secondary users.

Goodput-Based Link Resource Adaptation for Reliable Packet Transmissions in BIC-OFDM Cognitive Radio Networks

Cognitive radio (CR) stands out as a potential cornerstone to break the spectrum gridlock through enabling the coexistence of licensed (primary) and unlicensed (secondary) users in the same bandwidth. This paper deals with a novel link resource adaptation (LRA) strategy to be applied in CR scenarios for reliable packet transmissions based on bit interleaved coded orthogonal frequency division multiplexing (BIC-OFDM). We first formulate the power allocation (PA) problem constrained by both the available power at the secondary transmitter (ST) and the interference tolerable at the primary receivers, aimed at maximizing the offered layer 3 data rate, i.e., the goodput (GP) metric. Then, we derive the optimal PA strategy resorting to the customary Lagrangian dual decomposition (LDD) technique, which, however, like many other conventional numerical methods, exhibits several drawbacks, such as slow convergence and need for parameter tuning. These restrictions are circumvented through the development of a novel iterative yet simple PA algorithm, referred to as successive set reduction (SSR) approach, whose optimality conditions are analytically demonstrated by resorting to the Quasi Variational Inequality (QVI) framework. Based on this PA algorithm, an adaptive modulation and coding (AMC) scheme at the ST is eventually derived. Simulation results over a realistic scenario corroborate the effectiveness of the proposed SSR-based AMC algorithm, highlighting the GP improvements over non-adaptive LRA techniques, besides a remarkable complexity reduction w.r.t. conventional numerical methods.

Energy efficient precoder design for MIMO-OFDM with rate-dependent circuit power

This paper studies an energy efficient design of precoders for point-to-point multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Differently from traditional approaches, the optimal power allocation strategy is studied by modelling the circuit power as a rate-dependent function. We show that if the circuit power is a constant plus an increasing and convex function of the transmission rate, the problem of minimizing the consumed energy per bit received can be reformulated as a convex fractional program and solved by means of a bisection algorithm. The impact of the some system parameters is investigated either analytically or by means of computational results.

Distributed energy-efficient power optimization for relay-aided heterogeneous networks

This paper presents an energy-efficient power allocation for relay-aided heterogeneous networks subject to coupling convex constraints, that make the problem at hand a generalized Nash equilibrium problem. The solution to the resource allocation problem is derived using a sequential penalty approach based on the advanced theory of quasi variational inequality, which allows the network to converge to its generalized Nash equilibrium in a distributed manner. The main feature of the proposed approach is its decomposability, which leads to a two-layer distributed algorithm with provable convergence.

Generalized multi-carrier: An efficient platform for cognitive wireless applications

In this paper the Generalized Multicarrier (GMC) based transmission has been proposed as a candidate for wireless cognitive applications. It has been proved that by relaxing the constraints typical for OFDM transmission (such as fixed time and frequency distance between the transmit pulses) one can gain not only in terms of spectral efficiency but also in terms of lower out-of-band radiation. Moreover, it has been proposed to solve one of the main problems of the GMC signaling, that is the presence of the so-called residual self-interference by means of iterative interference cancellation (IIC) methods. The performance in terms of BER and averaged transmit power of the GMC system has been compared with OFDM one in the presence of the fading channel. In both cases, the pragmatic bit-and-power loading scheme based on the Campello algorithm have been considered and the IIC based receivers have been tested.